Research on Knowledge Graph-Aware Offloading Optimization and Content Acquisition Methods for Air-Ground Collaboration in VANETs

With the rapid development of 5G/6G and IoV technologies, the multi-temporal variable parametric relationships between devices and unused idle resources at the edge in highly dynamic IoV can affect the QoS experience of users. Therefore, we construct a framework for air-ground collaborative offloading and content acquisition model first in edge VANETs environment, where users are able to select compute and cache content resources from different edge nodes for edge services. Secondly, we propose a dynamic orchestration algorithm for knowledge graphs of edge services based on multitemporal variable parametric knowledge relations which reduces the interference of redundant information. Then, a non-convex optimization problem is established by considering the system utility function weighted by multiple performance metrics under environmental constraints and different service policies, and the problem is transformed and analyzed by a theoretical perspective using block coordinate descent and successive convex approximation methods to obtain the theoretical solution. In terms of simulation, we propose a knowledge graph-aware air-ground collaborative offloading and content acquisition SAC algorithm in VANETs (VAKOCS) to obtain the simulation solution of the problem and validate the problem in both directions. The proposed VAKOCS algorithm improves the network reward by 6.5%, 23%, 47% and 72%, respectively. Task offloading delay is reduced by 32%, 63%, 78% and 87%, respectively, and the content rental utility is reduced by 6%, 51% and 72%, respectively, while the error between the simulation solution and the theoretical solution is 0.45.